Previous mass spectrometry (MS)-based global proteomics studies have detected a combined total of 86% of all Treponema pallidum proteins under infection conditions (in vivo-grown T. pallidum). Recently, a method was developed for the long-term culture of T. pallidum under in vitro conditions (in vitro-cultured T. pallidum). Herein, we used our previously reported optimized MS-based proteomics approach to characterize the T. pallidum global protein expression profile under in vitro culture conditions. These analyses provided a proteome coverage of 94%, which extends the combined T. pallidum proteome coverage from the previously reported 86% to a new combined total of 95%. This study provides a more complete understanding of the protein repertoire of T. pallidum. Further, comparison of the in vitro-expressed proteome with the previously determined in vivo-expressed proteome identifies only a few proteomic changes between the two growth conditions, reinforcing the suitability of in vitro-cultured T. pallidum as an alternative to rabbit-based treponemal growth. The MS proteomics data have been deposited in the MassIVE repository with the data set identifier MSV000093603 (ProteomeXchange identifier PXD047625).

Mass spectrometry Interactive Virtual Environment (MassIVE) is a community resource developed by the NIH-funded Center for Computational Mass Spectrometry to promote the global, free exchange of mass spectrometry data. Data repository for proteomics data.

Global bottom-up mass spectrometry (MS)-based proteomics is widely used for protein identification and quantification to achieve a comprehensive understanding of the composition, structure, and function of the proteome. However, traditional sample preparation methods are time-consuming, typically including overnight tryptic digestion, extensive sample cleanup to remove MS-incompatible surfactants, and offline sample fractionation to reduce proteome complexity prior to online liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis. Thus, there is a need for a fast, robust, and reproducible method for protein identification and quantification from complex proteomes. Herein, we developed an ultrafast bottom-up proteomics method enabled by Azo, a photocleavable, MS-compatible surfactant that effectively solubilizes proteins and promotes rapid tryptic digestion, combined with the Bruker timsTOF Pro, which enables deeper proteome coverage through trapped ion mobility spectrometry (TIMS) and parallel accumulation–serial fragmentation (PASEF) of peptides. We applied this method to analyze the complex human cardiac proteome and identified nearly 4000 protein groups from as little as 1 mg of human heart tissue in a single one-dimensional LC-TIMS-MS/MS run with high reproducibility. Overall, we anticipate this ultrafast, robust, and reproducible bottom-up method empowered by both Azo and the timsTOF Pro will be generally applicable and greatly accelerate the throughput of large-scale quantitative proteomic studies. Raw data are available via the MassIVE repository with identifier MSV000087476.

Kinase enrichment utilizing broad-spectrum kinase inhibitors enables the identification of large proportions of the expressed kinome by mass spectrometry. However, the existing inhibitors are still inadequate in covering the entire kinome. Here, we identified a novel bisanilino pyrimidine, CTx-0294885, exhibiting inhibitory activity against a broad range of kinases in vitro, and further developed it into a Sepharose-supported kinase capture reagent. Use of a quantitative proteomics approach confirmed the selectivity of CTx-0294885-bound beads for kinase enrichment. Large-scale CTx-0294885-based affinity purification followed by LC–MS/MS led to the identification of 235 protein kinases from MDA-MB-231 cells, including all members of the AKT family that had not been previously detected by other broad-spectrum kinase inhibitors. Addition of CTx-0294885 to a mixture of three kinase inhibitors commonly used for kinase-enrichment increased the number of kinase identifications to 261, representing the largest kinome coverage from a single cell line reported to date. Coupling phosphopeptide enrichment with affinity purification using the four inhibitors enabled the identification of 799 high-confidence phosphosites on 183 kinases, ∼10% of which were localized to the activation loop, and included previously unreported phosphosites on BMP2K, MELK, HIPK2, and PRKDC. Therefore, CTx-0294885 represents a powerful new reagent for analysis of kinome signaling networks that may facilitate development of targeted therapeutic strategies. Proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the data set identifier PXD000239.

Curation and ISA representation of a SARS-Cov2/Covid-19 Proteomics Dataset deposited in PRIDE database with accession number: PXD107710

ISA-Tab annotation for the "SARS-CoV-2 infected host cell proteomics reveal potential therapy targets" publication.

Github repository: https://github.com/ISA-tools/PXD017710

This is part of an effort to (re-)annotate: https://dx.doi.org/10.21203/rs.3.rs-17218/v1

Additional work done as part of:

https://github.com/virtual-biohackathons/covid-19-bh20

https://github.com/virtual-biohackathons/covid-19-bh20/wiki/FairData

Proteomics data

Available from PRIDE at https://www.ebi.ac.uk/pride/archive/projects/PXD017710 and [MassIVE/CCMS Maestro+MSstats reanalysis of MSV000085096 / PXD017710]

ISA-Tab representation:

Rationale: Demonstrate suitability of the ISA format for representing MS based protein profiling experiment with more granularity and details, thus providing a better representation of the experiment design. The formatting and re-annotation are based on information extracted from: - the original publication - the supplementary tables available from the publishers site - the 'filtered-results.csv' helper file as supplied to @sneumann during the HUPO-PSI meeting March 2020

Viewing the ISA-tab formatted and re-annotated PXD017710 with ISATab-Viewer

Viewing the ISA-tab formatted and re-annotated PXD017710 locally, do the following:

python -m http.server 8000

Then point your browser to http://0.0.0.0:8000/isaviewer-demo.html

Curation tasks performed:

• initial structure of the study design in ISA format:

• linkage of Proteome and Translatome data (supplementary material) to ISA assay tables (via Derived Data File)

• processing the Proteome and Translatome data (supplementary material) with python pandas library to generate the following csv files:

  • proteome_intensities_long_table_ggplot2.txt
  • proteome_diffanal_ratio_pvalue_long_table_ggplot2.txt
  • translatome_intensities_long_table_ggplot2.txt
  • translatome_diffanal_ratio_pvalue_long_table_ggplot2

  The files are long table corresponding to a melt on the Excel file originally generated by the users and can be readily loaded in R ggplot2 library for graphical representation. The statistical relevant elements have been annotated with the STATO ontology and the tables comply with a Frictionless.io Data Package. The jupyter notebook for the transformation is available.

• conversion of raw data to mzML format: detailed in https://github.com/ISA-tools/PXD017710

install docker: bash >brew update >brew install docker

sign in to docker bash >docker start >docker login

pull docker container for ProteoWizard: ```bash

docker pull chambm/pwiz-i-agree-to-the-vendor-licenses ```

:warning: be sure to sign-up and login to https://hub.docker.com/

in order to be able to reach

https://hub.docker.com/r/chambm/pwiz-skyline-i-agree-to-the-vendor-licenses

run the pwiz tool from the container over the raw data: bash docker run -it --rm -e WINEDEBUG=-all -v /Users/Downloads/PXD017710/raw/:/data chambm/pwiz-skyline-i-agree-to-the-vendor-licenses wine msconvert /data/*.raw --mzML

• ontology markup for:
  • declaration of independent variables as ISA Study Factors:{biological agent, dose, time point, replicate} ->OBI
  • Taxonomic information (host cells and virus) -> NCBITaxonomy
  • Cell line: CaCo-2 cells -> Cell Line Ontology
  • Disease: Colon Cancer -> Human Phenotype Ontology
  • MS specific aspect (TMT reagent, instrument ... ) -> PSI-MS
  • Statistical Tests -> STATO

Unresolved curatorial issues:

1. ambiguities related to Tandem Mass Tag labelling protocol

   • the publication mentions TMT11 (see Figure 2 in https://www.researchsquare.com/article/rs-17218/v1)
   • the information available from PRIDE mentions TMT6 (https://www.ebi.ac.uk/pride/archive/projects/PXD017710) This may require another round of annotation on the TMT agents and fractions in the ISA a_assay representation

2. SARS-Cov2 isolate: no clear NCBI Taxonomic anchoring and unclear origin: -> the markup is made to the parent class (as of 06.04.2020)

Release and packaging as a BDBAG:

The tgz file associated with this upload has been producing using https://github.com/fair-research/bdbag. It contains several manifest files detailing metadata and data files, providing md5 and sha256 checksums.

Github repository: https://github.com/ISA-tools/PXD017710

Human pluripotent stem-cell-derived cardiomyocytes (hPSC-CMs) show immense promise for patient-specific disease modeling, cardiotoxicity screening, and regenerative therapy development. However, thus far, hPSC-CMs in culture have not recapitulated the structural or functional properties of adult CMs in vivo. To gain global insight into hPSC-CM biology, we established a multiomics method for analyzing the hPSC-CM metabolome and proteome from the same cell culture, creating multidimensional profiles of hPSC-CMs. Specifically, we developed a sequential extraction to capture metabolites and proteins from the same hPSC-CM monolayer cultures and analyzed these extracts using high-resolution mass spectrometry. Using this method, we annotated 205 metabolites/lipids and 4319 proteins from 106 cells with high reproducibility. We further integrated the proteome and metabolome measurements to create network profiles of molecular phenotypes for hPSC-CMs. Out of 310 pathways identified using metabolomics and proteomics, 40 pathways were considered significantly overrepresented (false-discovery-rate-corrected p ≤ 0.05). Highly populated pathways included those involved in protein synthesis (ribosome, spliceosome), ATP generation (oxidative phosphorylation), and cardiac muscle contraction. This multiomics method achieves a deep coverage of metabolites and proteins, creating a multidimensional view of the hPSC-CM phenotype, which provides a strong technological foundation to advance the understanding of hPSC-CM biology. Raw data are available in the MassIVE repository with identifier MSV000088010.

IQGAP1 (IQ motif-containing GTPase-activating protein 1) scaffolds several signaling pathways in mammalian cells that are implicated in carcinogenesis, including the RAS and PI3K pathways that involve multiple protein kinases. IQGAP1 has been shown to promote head and neck squamous cell carcinoma (HNSCC); however, the underlying mechanism(s) remains unclear. Here, we report a mass spectrometry-based analysis identifying differences in phosphorylation of cellular proteins in vivo and in vitro in the presence or absence of IQGAP1. By comparing the esophageal phosphoproteome profiles between Iqgap1+/+ and Iqgap1–/– mice, we identified RNA splicing as one of the most altered cellular processes. Serine/arginine-rich splicing factor 6 (SRSF6) was the protein with the most downregulated levels of phosphorylation in Iqgap1–/– tissue. We confirmed that the absence of IQGAP1 reduced SRSF6 phosphorylation both in vivo and in vitro. We then expanded our analysis to human normal oral keratinocytes. Again, we found factors involved in RNA splicing to be highly altered in the phosphoproteome profile upon genetic disruption of IQGAP1. Both the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Cancer Genome Atlas (TCGA) data sets indicate that phosphorylation of splicing-related proteins is important in HNSCC prognosis. The Biological General Repository for Interaction Datasets (BioGRID) repository also suggested multiple interactions between IQGAP1 and splicing-related proteins. Based on these collective observations, we propose that IQGAP1 regulates the phosphorylation of splicing proteins, which potentially affects their splicing activities and, therefore, contributes to HNSCC. Raw data are available from the MassIVE database with identifier MSV000087770.

Chemical cross-linking (XL) coupled to mass spectrometry (MS) has become a powerful approach to probe the structure of protein assemblies. Although most of the applications concerned purified complexes, latest developments focus on large-scale in vivo studies. Pushing in this direction, we developed an advanced in vivo cross-linking mass spectrometry platform to study the cellular interactome of living bacterial cells. It is based on in vivo labeling and involves a one-step enrichment by click chemistry on a solid support. Our approach shows an impressive efficiency on Neisseria meningitidis, leading to the identification of about 3300 cross-links for the LC-MS/MS analysis of a biological triplicate using a benchtop high-resolution Orbitrap mass spectrometer. Highly dynamic multiprotein complexes were successfully captured and characterized in all bacterial compartments, showing the great potential and precision of our proteome-wide approach. Our workflow paves new avenues for the large-scale and nonbiased analysis of protein–protein interactions. All raw data, databases, and processing parameters are available on ProteomeXchange via PRIDE repository (data set identifier PXD021553).

SummaryThe data described in this metadata record underlie the results and figures in the related manuscript. The data pertain to clinical, immunophenotypic, and proteomics profiles of 31 histologically confirmed low-grade phyllodes tumor and 30 fibroadenomas.The related study aimed to evaluate the clinical, histopathologic, immunophenotypic, and proteomic characteristics of low-grade phyllodes tumors and fibroadenomas to exploit similarities and differences them.The data files underlying the related study are as follows.- Clinical characteristics are included in the supplementary information of the article, as Supplementary Table 1.- Images of histologic features are found as figures in the related manuscript.- Overview data of the comparison between phyllodes tumor and fibroadenoma can be found in the file Phyllodes Data.xlsx, which is included with this metadata record.- The proteomics data underlying Figures 7 and 8 can be found in the MassIVE repository under accession code MSV000085409: https://identifiers.org/massive:MSV000085409Name of Institutional Review Board or ethics committee that approved the studyThe study was approved by the Washington University School of Medicine Institutional Review Board.

Microsampling of patient blood promises several benefits over conventional phlebotomy practices to facilitate precision medicine studies. These include at-home patient blood collection, supporting telehealth monitoring, minimal postcollection processing, and compatibility with nonrefrigerated transport and storage. However, for proteomic biomarker studies, mass spectrometry of whole blood has generally been avoided in favor of using plasma or serum obtained from venepuncture. We evaluated the use of a volumetric absorptive microsampling (VAMS) device as a sample preparation matrix to enable LC-MS proteomic analyses of dried whole blood. We demonstrated the detection and robust quantitation of up to 1600 proteins from single-shot shotgun-LC-MS analysis of dried whole blood, greatly enhancing proteome depth compared with conventional single-shot LC-MS analyses of undepleted plasma. Some proteins not previously reported in blood were detected using this approach. Various washing reagents were used to demonstrate that proteins can be preferentially removed from VAMS devices prior to downstream analyses. We provide a demonstration that archival frozen blood cell pellets housed under long-term storage (exceeding 5 years) are compatible with VAMS to enable quantitation of potential biomarker proteins from biobank repositories. These demonstrations are important steps in establishing viable analysis workflows to underpin large-scale precision medicine studies. Data are available via ProteomeXchange with the identifier PXD028605.

Compensatory islet response is a distinct feature of the prediabetic insulin-resistant state in humans and rodents. To identify alterations in the islet proteome that characterize the adaptive response, we analyzed islets from 5 month old male control, high-fat diet fed (HFD), or obese ob/ob mice by LC–MS/MS and quantified ∼1100 islet proteins (at least two peptides) with a false discovery rate < 1%. Significant alterations in abundance were observed for ∼350 proteins among groups. The majority of alterations were common to both models, and the changes of a subset of ∼40 proteins and 12 proteins were verified by targeted quantification using selected reaction monitoring and western blots, respectively. The insulin-resistant islets in both groups exhibited reduced expression of proteins controlling energy metabolism, oxidative phosphorylation, hormone processing, and secretory pathways. Conversely, an increased expression of molecules involved in protein synthesis and folding suggested effects in endoplasmic reticulum stress response, cell survival, and proliferation in both insulin-resistant models. In summary, we report a unique comparison of the islet proteome that is focused on the compensatory response in two insulin-resistant rodent models that are not overtly diabetic. These data provide a valuable resource of candidate proteins to the scientific community to undertake further studies aimed at enhancing β-cell mass in patients with diabetes. The data are available via the MassIVE repository, under accession no. MSV000079093.